Structural and magnetic properties of E-Fe_{1-x}Co_xSi thin films deposited via pulsed laser deposition

We report pulsed laser deposition synthesis and characterization of polycrystalline Fe1-xCox Si thin films on Si (111). X-ray diffraction, transmission electron, and atomic force microscopies reveal films to be dense, very smooth, and single phase with a cubic B20 crystal structure. Ferromagnetism with significant magnetic hysteresis is found for all films including nominally pure FeSi films in contrast to the very weak paramagnetism of bulk FeSi. For Fe1-xCoxSi this signifies a change from helimagnetism in bulk, to ferromagnetism in thin films. These ferromagnetic thin films are promising as a magnetic-silicide/silicon system for polarized current production, manipulation, and detection.Comment: 12 pages, 4 figures accepted in the Applied Physics Letter

Performance de la modélisation hybride sur un processus de défaillance dans les systèmes industriels

Une approche hybride est proposée pour établir le diagnostic d'un moteur électrique. L'approche se caractérise par un assemblage entre la modélisation physique du système réel et la mise en place d'un algorithme d'apprentissage automatique, en vue d'améliorer les performances de diagnostic. Mots-clefs-Modèle hybride, apprentissage automatique, modèle basé sur la connaissance, processus de défaillance, calcul haute performanc

Structural and magnetic properties of ε -Fe1-x Cox Si thin films deposited via pulsed laser deposition

We report pulsed laser deposition synthesis and characterization of polycrystalline Fe1-x Cox Si thin films on Si (111). X-ray diffraction, transmission electron, and atomic force microscopies reveal films to be dense, very smooth, and single phase with a cubic B20 crystal structure. Ferromagnetism with significant magnetic hysteresis is found for all films including nominally pure FeSi films in contrast to the very weak paramagnetism of bulk FeSi. For Fe1-x Cox Si this signifies a change from helimagnetism in bulk, to ferromagnetism in thin films. These ferromagnetic thin films are promising as a magnetic-silicide/silicon system for polarized current production, manipulation, and detection. © 2009 American Institute of Physics

A comparative study of qualitative and quantitative dynamic models of biological regulatory networks

Backgroun

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Risk factors for Coronavirus disease 2019 (Covid-19) death in a population cohort study from the Western Cape province, South Africa

Risk factors for coronavirus disease 2019 (COVID-19) death in sub-Saharan Africa and the effects of human immunodeficiency virus (HIV) and tuberculosis on COVID-19 outcomes are unknown. We conducted a population cohort study using linked data from adults attending public-sector health facilities in the Western Cape, South Africa. We used Cox proportional hazards models, adjusted for age, sex, location, and comorbidities, to examine the associations between HIV, tuberculosis, and COVID-19 death from 1 March to 9 June 2020 among (1) public-sector “active patients” (≥1 visit in the 3 years before March 2020); (2) laboratory-diagnosed COVID-19 cases; and (3) hospitalized COVID-19 cases. We calculated the standardized mortality ratio (SMR) for COVID-19, comparing adults living with and without HIV using modeled population estimates.Among 3 460 932 patients (16% living with HIV), 22 308 were diagnosed with COVID-19, of whom 625 died. COVID19 death was associated with male sex, increasing age, diabetes, hypertension, and chronic kidney disease. HIV was associated with COVID-19 mortality (adjusted hazard ratio [aHR], 2.14; 95% confidence interval [CI], 1.70–2.70), with similar risks across strata of viral loads and immunosuppression. Current and previous diagnoses of tuberculosis were associated with COVID-19 death (aHR, 2.70 [95% CI, 1.81–4.04] and 1.51 [95% CI, 1.18–1.93], respectively). The SMR for COVID-19 death associated with HIV was 2.39 (95% CI, 1.96–2.86); population attributable fraction 8.5% (95% CI, 6.1–11.1)

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Taking the pulse of Earth's tropical forests using networks of highly distributed plots

Tropical forests are the most diverse and productive ecosystems on Earth. While better understanding of these forests is critical for our collective future, until quite recently efforts to measure and monitor them have been largely disconnected. Networking is essential to discover the answers to questions that transcend borders and the horizons of funding agencies. Here we show how a global community is responding to the challenges of tropical ecosystem research with diverse teams measuring forests tree-by-tree in thousands of long-term plots. We review the major scientific discoveries of this work and show how this process is changing tropical forest science. Our core approach involves linking long-term grassroots initiatives with standardized protocols and data management to generate robust scaled-up results. By connecting tropical researchers and elevating their status, our Social Research Network model recognises the key role of the data originator in scientific discovery. Conceived in 1999 with RAINFOR (South America), our permanent plot networks have been adapted to Africa (AfriTRON) and Southeast Asia (T-FORCES) and widely emulated worldwide. Now these multiple initiatives are integrated via ForestPlots.net cyber-infrastructure, linking colleagues from 54 countries across 24 plot networks. Collectively these are transforming understanding of tropical forests and their biospheric role. Together we have discovered how, where and why forest carbon and biodiversity are responding to climate change, and how they feedback on it. This long-term pan-tropical collaboration has revealed a large long-term carbon sink and its trends, as well as making clear which drivers are most important, which forest processes are affected, where they are changing, what the lags are, and the likely future responses of tropical forests as the climate continues to change. By leveraging a remarkably old technology, plot networks are sparking a very modern revolution in tropical forest science. In the future, humanity can benefit greatly by nurturing the grassroots communities now collectively capable of generating unique, long-term understanding of Earth's most precious forests.Additional co-authors: Susan Laurance, William Laurance, Francoise Yoko Ishida, Andrew Marshall, Catherine Waite, Hannsjoerg Woell, Jean-Francois Bastin, Marijn Bauters, Hans Beeckman, Pfascal Boeckx, Jan Bogaert, Charles De Canniere, Thales de Haulleville, Jean-Louis Doucet, Olivier Hardy, Wannes Hubau, Elizabeth Kearsley, Hans Verbeeck, Jason Vleminckx, Steven W. Brewer, Alfredo Alarcón, Alejandro Araujo-Murakami, Eric Arets, Luzmila Arroyo, Ezequiel Chavez, Todd Fredericksen, René Guillén Villaroel, Gloria Gutierrez Sibauty, Timothy Killeen, Juan Carlos Licona, John Lleigue, Casimiro Mendoza, Samaria Murakami, Alexander Parada Gutierrez, Guido Pardo, Marielos Peña-Claros, Lourens Poorter, Marisol Toledo, Jeanneth Villalobos Cayo, Laura Jessica Viscarra, Vincent Vos, Jorge Ahumada, Everton Almeida, Jarcilene Almeida, Edmar Almeida de Oliveira, Wesley Alves da Cruz, Atila Alves de Oliveira, Fabrício Alvim Carvalho, Flávio Amorim Obermuller, Ana Andrade, Fernanda Antunes Carvalho, Simone Aparecida Vieira, Ana Carla Aquino, Luiz Aragão, Ana Claudia Araújo, Marco Antonio Assis, Jose Ataliba Mantelli Aboin Gomes, Fabrício Baccaro, Plínio Barbosa de Camargo, Paulo Barni, Jorcely Barroso, Luis Carlos Bernacci, Kauane Bordin, Marcelo Brilhante de Medeiros, Igor Broggio, José Luís Camargo, Domingos Cardoso, Maria Antonia Carniello, Andre Luis Casarin Rochelle, Carolina Castilho, Antonio Alberto Jorge Farias Castro, Wendeson Castro, Sabina Cerruto Ribeiro, Flávia Costa, Rodrigo Costa de Oliveira, Italo Coutinho, John Cunha, Lola da Costa, Lucia da Costa Ferreira, Richarlly da Costa Silva, Marta da Graça Zacarias Simbine, Vitor de Andrade Kamimura, Haroldo Cavalcante de Lima, Lia de Oliveira Melo, Luciano de Queiroz, José Romualdo de Sousa Lima, Mário do Espírito Santo, Tomas Domingues, Nayane Cristina dos Santos Prestes, Steffan Eduardo Silva Carneiro, Fernando Elias, Gabriel Eliseu, Thaise Emilio, Camila Laís Farrapo, Letícia Fernandes, Gustavo Ferreira, Joice Ferreira, Leandro Ferreira, Socorro Ferreira, Marcelo Fragomeni Simon, Maria Aparecida Freitas, Queila S. García, Angelo Gilberto Manzatto, Paulo Graça, Frederico Guilherme, Eduardo Hase, Niro Higuchi, Mariana Iguatemy, Reinaldo Imbrozio Barbosa, Margarita Jaramillo, Carlos Joly, Joice Klipel, Iêda Leão do Amaral, Carolina Levis, Antonio S. Lima, Maurício Lima Dan, Aline Lopes, Herison Madeiros, William E. Magnusson, Rubens Manoel dos Santos, Beatriz Marimon, Ben Hur Marimon Junior, Roberta Marotti Martelletti Grillo, Luiz Martinelli, Simone Matias Reis, Salomão Medeiros, Milton Meira-Junior, Thiago Metzker, Paulo Morandi, Natanael Moreira do Nascimento, Magna Moura, Sandra Cristina Müller, Laszlo Nagy, Henrique Nascimento, Marcelo Nascimento, Adriano Nogueira Lima, Raimunda Oliveira de Araújo, Jhonathan Oliveira Silva, Marcelo Pansonato, Gabriel Pavan Sabino, Karla Maria Pedra de Abreu, Pablo José Francisco Pena Rodrigues, Maria Piedade, Domingos Rodrigues, José Roberto Rodrigues Pinto, Carlos Quesada, Eliana Ramos, Rafael Ramos, Priscyla Rodrigues, Thaiane Rodrigues de Sousa, Rafael Salomão, Flávia Santana, Marcos Scaranello, Rodrigo Scarton Bergamin, Juliana Schietti, Jochen Schöngart, Gustavo Schwartz, Natalino Silva, Marcos Silveira, Cristiana Simão Seixas, Marta Simbine, Ana Claudia Souza, Priscila Souza, Rodolfo Souza, Tereza Sposito, Edson Stefani Junior, Julio Daniel do Vale, Ima Célia Guimarães Vieira, Dora Villela, Marcos Vital, Haron Xaud, Katia Zanini, Charles Eugene Zartman, Nur Khalish Hafizhah Ideris, Faizah binti Hj Metali, Kamariah Abu Salim, Muhd Shahruney Saparudin, Rafizah Mat Serudin, Rahayu Sukmaria Sukri, Serge Begne, George Chuyong, Marie Noel Djuikouo, Christelle Gonmadje, Murielle Simo-Droissart, Bonaventure Sonké, Hermann Taedoumg, Lise Zemagho, Sean Thomas, Fidèle Baya, Gustavo Saiz, Javier Silva Espejo, Dexiang Chen, Alan Hamilton, Yide Li, Tushou Luo, Shukui Niu, Han Xu, Zhang Zhou, Esteban Álvarez-Dávila, Juan Carlos Andrés Escobar, Henry Arellano-Peña, Jaime Cabezas Duarte, Jhon Calderón, Lina Maria Corrales Bravo, Borish Cuadrado, Hermes Cuadros, Alvaro Duque, Luisa Fernanda Duque, Sandra Milena Espinosa, Rebeca Franke-Ante, Hernando García, Alejandro Gómez, Roy González-M., Álvaro Idárraga-Piedrahíta, Eliana Jimenez, Rubén Jurado, Wilmar López Oviedo, René López-Camacho, Omar Aurelio Melo Cruz, Irina Mendoza Polo, Edwin Paky, Karen Pérez, Angel Pijachi, Camila Pizano, Adriana Prieto, Laura Ramos, Zorayda Restrepo Correa, James Richardson, Elkin Rodríguez, Gina M. Rodriguez M., Agustín Rudas, Pablo Stevenson, Markéta Chudomelová, Martin Dancak, Radim Hédl, Stanislav Lhota, Martin Svatek, Jacques Mukinzi, Corneille Ewango, Terese Hart, Emmanuel Kasongo Yakusu, Janvier Lisingo, Jean-Remy Makana, Faustin Mbayu, Benjamin Toirambe, John Tshibamba Mukendi, Lars Kvist, Gustav Nebel, Selene Báez, Carlos Céron, Daniel M. Griffith, Juan Ernesto Guevara Andino, David Neill, Walter Palacios, Maria Cristina Peñuela-Mora, Gonzalo Rivas-Torres, Gorky Villa, Sheleme Demissie, Tadesse Gole, Techane Gonfa, Kalle Ruokolainen, Michel Baisie, Fabrice Bénédet, Wemo Betian, Vincent Bezard, Damien Bonal, Jerôme Chave, Vincent Droissart, Sylvie Gourlet-Fleury, Annette Hladik, Nicolas Labrière, Pétrus Naisso, Maxime Réjou-Méchain, Plinio Sist, Lilian Blanc, Benoit Burban, Géraldine Derroire, Aurélie Dourdain, Clement Stahl, Natacha Nssi Bengone, Eric Chezeaux, Fidèle Evouna Ondo, Vincent Medjibe, Vianet Mihindou, Lee White, Heike Culmsee, Cristabel Durán Rangel, Viviana Horna, Florian Wittmann, Stephen Adu-Bredu, Kofi Affum-Baffoe, Ernest Foli, Michael Balinga, Anand Roopsind, James Singh, Raquel Thomas, Roderick Zagt, Indu K. Murthy, Kuswata Kartawinata, Edi Mirmanto, Hari Priyadi, Ismayadi Samsoedin, Terry Sunderland, Ishak Yassir, Francesco Rovero, Barbara Vinceti, Bruno Hérault, Shin-Ichiro Aiba, Kanehiro Kitayama, Armandu Daniels, Darlington Tuagben, John T. Woods, Muhammad Fitriadi, Alexander Karolus, Kho Lip Khoon, Noreen Majalap, Colin Maycock, Reuben Nilus, Sylvester Tan, Almeida Sitoe, Indiana Coronado G., Lucas Ojo, Rafael de Assis, Axel Dalberg Poulsen, Douglas Sheil, Karen Arévalo Pezo, Hans Buttgenbach Verde, Victor Chama Moscoso, Jimmy Cesar Cordova Oroche, Fernando Cornejo Valverde, Massiel Corrales Medina, Nallaret Davila Cardozo, Jano de Rutte Corzo, Jhon del Aguila Pasquel, Gerardo Flores Llampazo, Luis Freitas, Darcy Galiano Cabrera, Roosevelt García Villacorta, Karina Garcia Cabrera, Diego García Soria, Leticia Gatica Saboya, Julio Miguel Grandez Rios, Gabriel Hidalgo Pizango, Eurídice Honorio Coronado, Isau Huamantupa-Chuquimaco, Walter Huaraca Huasco, Yuri Tomas Huillca Aedo, Jose Luis Marcelo Peña, Abel Monteagudo Mendoza, Vanesa Moreano Rodriguez, Percy Núñez Vargas, Sonia Cesarina Palacios Ramos, Nadir Pallqui Camacho, Antonio Peña Cruz, Freddy Ramirez Arevalo, José Reyna Huaymacari, Carlos Reynel Rodriguez, Marcos Antonio Ríos Paredes, Lily Rodriguez Bayona, Rocio del Pilar Rojas Gonzales, Maria Elena Rojas Peña, Norma Salinas Revilla, Yahn Carlos Soto Shareva, Raul Tupayachi Trujillo, Luis Valenzuela Gamarra, Rodolfo Vasquez Martinez, Jim Vega Arenas, Christian Amani, Suspense Averti Ifo, Yannick Bocko, Patrick Boundja, Romeo Ekoungoulou, Mireille Hockemba, Donatien Nzala, Alusine Fofanah, David Taylor, Guillermo Bañares-de Dios, Luis Cayuela, Íñigo Granzow-de la Cerda, Manuel Macía, Juliana Stropp, Maureen Playfair, Verginia Wortel, Toby Gardner, Robert Muscarella, Hari Priyadi, Ervan Rutishauser, Kuo-Jung Chao, Pantaleo Munishi, Olaf Bánki, Frans Bongers, Rene Boot, Gabriella Fredriksson, Jan Reitsma, Hans ter Steege, Tinde van Andel, Peter van de Meer, Peter van der Hout, Mark van Nieuwstadt, Bert van Ulft, Elmar Veenendaal, Ronald Vernimmen, Pieter Zuidema, Joeri Zwerts, Perpetra Akite, Robert Bitariho, Colin Chapman, Eilu Gerald, Miguel Leal, Patrick Mucunguzi, Miguel Alexiades, Timothy R. Baker, Karina Banda, Lindsay Banin, Jos Barlow, Amy Bennett, Erika Berenguer, Nicholas Berry, Neil M. Bird, George A. Blackburn, Francis Brearley, Roel Brienen, David Burslem, Lidiany Carvalho, Percival Cho, Fernanda Coelho, Murray Collins, David Coomes, Aida Cuni-Sanchez, Greta Dargie, Kyle Dexter, Mat Disney, Freddie Draper, Muying Duan, Adriane Esquivel-Muelbert, Robert Ewers, Belen Fadrique, Sophie Fauset, Ted R. Feldpausch, Filipe França, David Galbraith, Martin Gilpin, Emanuel Gloor, John Grace, Keith Hamer, David Harris, Tommaso Jucker, Michelle Kalamandeen, Bente Klitgaard, Aurora Levesley, Simon L. Lewis, Jeremy Lindsell, Gabriela Lopez-Gonzalez, Jon Lovett, Yadvinder Malhi, Toby Marthews, Emma McIntosh, Karina Melgaço, William Milliken, Edward Mitchard, Peter Moonlight, Sam Moore, Alexandra Morel, Julie Peacock, Kelvin Peh, Colin Pendry, R. Toby Pennington, Luciana de Oliveira Pereira, Carlos Peres, Oliver L. Phillips, Georgia Pickavance, Thomas Pugh, Lan Qie, Terhi Riutta, Katherine Roucoux, Casey Ryan, Tiina Sarkinen, Camila Silva Valeria, Dominick Spracklen, Suzanne Stas, Martin Sullivan, Michael Swaine, Joey Talbot, James Taplin, Geertje van der Heijden, Laura Vedovato, Simon Willcock, Mathew Williams, Luciana Alves, Patricia Alvarez Loayza, Gabriel Arellano, Cheryl Asa, Peter Ashton, Gregory Asner, Terry Brncic, Foster Brown, Robyn Burnham, Connie Clark, James Comiskey, Gabriel Damasco, Stuart Davies, Tony Di Fiore, Terry Erwin, William Farfan-Rios, Jefferson Hall, David Kenfack, Thomas Lovejoy, Roberta Martin, Olga Martha Montiel, John Pipoly, Nigel Pitman, John Poulsen, Richard Primack, Miles Silman, Marc Steininger, Varun Swamy, John Terborgh, Duncan Thomas, Peter Umunay, Maria Uriarte, Emilio Vilanova Torre, Ophelia Wang, Kenneth Young, Gerardo A. Aymard C., Lionel Hernández, Rafael Herrera Fernández, Hirma Ramírez-Angulo, Pedro Salcedo, Elio Sanoja, Julio Serrano, Armando Torres-Lezama, Tinh Cong Le, Trai Trong Le, Hieu Dang Tra

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival